LSOA domestic Electricty and Gas maps
Meghan Kinglsey-Walsh
Last run at: 2021-08-26 17:54:48
1 Mapping the Lower Layer Super Output Area (LSOA)
1.1 Mapping Domestic
1.1.1 Mapping Electricity consumption
####2019
#Electricty consumption data LSOA pre downloaded
inFile <-paste0(dataFolder, "energy/electricity/LSOA Dom Elec csv/LSOA_ELEC_2019.csv")
lsoa_elecData <- readr::read_csv(inFile)##
## -- Column specification --------------------------------------------------------
## cols(
## `Local Authority Name` = col_character(),
## `Local Authority Code` = col_character(),
## `Middle Layer Super Output Area (MSOA) Name` = col_character(),
## `Middle Layer Super Output Area (MSOA) Code` = col_character(),
## `Lower Layer Super Output Area (LSOA) Name` = col_character(),
## `Lower Layer Super Output Area (LSOA) Code` = col_character(),
## `Total number of domestic electricity meters` = col_double(),
## `Total domestic electricity consumption (kWh)` = col_double(),
## `Mean domestic electricity consumption
## (kWh per meter)` = col_double(),
## `Median domestic electricity consumption
## (kWh per meter)` = col_double()
## )lsoa_elecData$LSOA11CD <- lsoa_elecData$`Lower Layer Super Output Area (LSOA) Code`
head(lsoa_elecData)## # A tibble: 6 x 11
## `Local Authority~ `Local Authority~ `Middle Layer Super ~ `Middle Layer Super~
## <chr> <chr> <chr> <chr>
## 1 Hartlepool E06000001 Hartlepool 001 E02002483
## 2 Hartlepool E06000001 Hartlepool 001 E02002483
## 3 Hartlepool E06000001 Hartlepool 001 E02002483
## 4 Hartlepool E06000001 Hartlepool 001 E02002483
## 5 Hartlepool E06000001 Hartlepool 001 E02002483
## 6 Hartlepool E06000001 Hartlepool 001 E02002483
## # ... with 7 more variables: Lower Layer Super Output Area (LSOA) Name <chr>,
## # Lower Layer Super Output Area (LSOA) Code <chr>,
## # Total number of domestic electricity meters <dbl>,
## # Total domestic electricity consumption (kWh) <dbl>,
## # Mean domestic electricity consumption (kWh per meter) <dbl>,
## # Median domestic electricity consumption (kWh per meter) <dbl>,
## # LSOA11CD <chr>#The LSOA boundaries for the Solent have been pre-downloaded
inf<-paste0(dataFolder, "boundaries/LSOA/lsoa_solent.shp")
message ("Loading LSOA boundaries from file")## Loading LSOA boundaries from filelsoa_sf_data <- sf::read_sf(inf)
head(lsoa_sf_data)## Simple feature collection with 6 features and 15 fields
## Geometry type: MULTIPOLYGON
## Dimension: XY
## Bounding box: xmin: 465991.4 ymin: 100849 xmax: 467770.5 ymax: 102360.8
## Projected CRS: OSGB 1936 / British National Grid
## # A tibble: 6 x 16
## LSOA11CD OBJECTID LSOA11NM LSOA11NMW BNG_E BNG_N LONG LAT Shape__Are
## <chr> <int> <chr> <chr> <int> <int> <dbl> <dbl> <dbl>
## 1 E01017013 16519 Portsmout~ Portsmouth~ 466127 101741 -1.06 50.8 145134.
## 2 E01017014 16520 Portsmout~ Portsmouth~ 466924 101957 -1.05 50.8 757371.
## 3 E01017015 16521 Portsmout~ Portsmouth~ 467084 101499 -1.05 50.8 472647.
## 4 E01017016 16522 Portsmout~ Portsmouth~ 466330 101597 -1.06 50.8 122927.
## 5 E01017017 16523 Portsmout~ Portsmouth~ 466813 101069 -1.05 50.8 166924.
## 6 E01017018 16524 Portsmout~ Portsmouth~ 466350 101184 -1.06 50.8 184945.
## # ... with 7 more variables: Shape__Len <dbl>, MSOA11CD <chr>, MSOA11NM <chr>,
## # LAD11CD <chr>, LAD11NM <chr>, nLSOAs <int>, geometry <MULTIPOLYGON [m]>table(lsoa_sf_data$LAD11NM)#How many LSOAs are there in each LA##
## Basingstoke and Deane East Hampshire Eastleigh
## 109 72 77
## Fareham Gosport Hart
## 73 53 57
## Havant Isle of Wight New Forest
## 78 89 114
## Portsmouth Southampton Test Valley
## 125 148 71
## Winchester
## 70lsoa_merged_sf <- merge(lsoa_sf_data, lsoa_elecData) #merging the boundaries and energy data
ggplot2::ggplot(lsoa_merged_sf) +
geom_sf(aes(fill = `Mean domestic electricity consumption \n(kWh per meter)`)) +
scale_fill_continuous(name = "Mean kWh per meter", low = "green", high = "red") +
labs(caption = "Solent (all LSOAs)")
#Mapping it####Map with Overlay
library(leaflet)
st_coord_sys <- st_crs(lsoa_merged_sf)
st_coord_sys$epsg## [1] 27700#Trasforming the coord systen
if(st_coord_sys$epsg !=4326){
map_df_trans <- st_transform(lsoa_merged_sf, "+proj=longlat +datum=WGS84")
}qpal <- colorNumeric("Reds", map_df_trans$`Mean domestic electricity consumption \n(kWh per meter)`, n=9)
leaflet(map_df_trans) %>%
addTiles() %>% # Add default OpenStreetMap map tiles
addPolygons(color = ~qpal(`Mean domestic electricity consumption \n(kWh per meter)`),
fillOpacity = 0.6, weight = 1.5, popup = ~(LSOA11CD),
highlight = highlightOptions(
weight = 5,
color = "#666",
fillOpacity = 0.7,
bringToFront = TRUE))1.1.1.0.1 Looking at cities in more detail
#Cities such as Southampton disappear due to their density, we will now map Southampton alone to see this area in more detail
mapData <- dplyr::filter(lsoa_merged_sf, LAD11NM == "Southampton")
# plotting the electricity consumption of Southampton in more detail
ggplot2::ggplot(mapData) +
geom_sf(aes(fill = `Mean domestic electricity consumption \n(kWh per meter)`)) +
scale_fill_continuous(name = "Mean kWh per meter", low = "green", high = "red") +
labs(caption = "Southampton")
#Cities such as Portsmouth disappear due to their density, we will now map Portsmouth alone to see this area in more detail
mapData <- dplyr::filter(lsoa_merged_sf, LAD11NM == "Portsmouth")
# plotting the electricity consumption of Portsmouth in more detail
ggplot2::ggplot(mapData) +
geom_sf(aes(fill = `Mean domestic electricity consumption \n(kWh per meter)`)) +
scale_fill_continuous(name = "Mean kWh per meter", low = "green", high = "red") +
labs(caption = "Portsmouth")
#Cities such as Winchester disappear due to their density, we will now map Winchester alone to see this area in more detail
mapData <- dplyr::filter(lsoa_merged_sf, LAD11NM == "Winchester")
# plotting the electricity consumption of Winchester in more detail
ggplot2::ggplot(mapData) +
geom_sf(aes(fill = `Mean domestic electricity consumption \n(kWh per meter)`)) +
scale_fill_continuous(name = "Mean kWh per meter", low = "green", high = "red") +
labs(caption = "Winchester")
####Difference between 2010 and 2019 dom elec
domelec_LSOA_2010 <- readr::read_csv(paste0(dataFolder,"energy/electricity/LSOA Dom Elec csv/LSOA_ELEC_2010.csv"))##
## -- Column specification --------------------------------------------------------
## cols(
## `Local Authority Name` = col_character(),
## `Local Authority Code` = col_character(),
## `Middle Layer Super Output Area (MSOA) Name` = col_character(),
## `Middle Layer Super Output Area (MSOA) Code` = col_character(),
## `Lower Layer Super Output Area (LSOA) Name` = col_character(),
## `Lower Layer Super Output Area (LSOA) Code` = col_character(),
## `Total number of domestic electricity meters` = col_double(),
## `Total domestic electricity consumption (kWh)` = col_double(),
## `Mean domestic electricity consumption
## (kWh per meter)` = col_double(),
## `Median domestic electricity consumption
## (kWh per meter)` = col_double()
## )domelec_LSOA_2019 <- readr::read_csv(paste0(dataFolder, "energy/electricity/LSOA Dom Elec csv/LSOA_ELEC_2019.csv"))##
## -- Column specification --------------------------------------------------------
## cols(
## `Local Authority Name` = col_character(),
## `Local Authority Code` = col_character(),
## `Middle Layer Super Output Area (MSOA) Name` = col_character(),
## `Middle Layer Super Output Area (MSOA) Code` = col_character(),
## `Lower Layer Super Output Area (LSOA) Name` = col_character(),
## `Lower Layer Super Output Area (LSOA) Code` = col_character(),
## `Total number of domestic electricity meters` = col_double(),
## `Total domestic electricity consumption (kWh)` = col_double(),
## `Mean domestic electricity consumption
## (kWh per meter)` = col_double(),
## `Median domestic electricity consumption
## (kWh per meter)` = col_double()
## )domelec_LSOA_2010$de2010 <- domelec_LSOA_2010$'Mean domestic electricity consumption \n(kWh per meter)'
domelec_LSOA_2019$de2019 <- domelec_LSOA_2019$'Mean domestic electricity consumption \n(kWh per meter)'
# create unambiguous lsoa code for matching to each other and the sf geometry
domelec_LSOA_2010$LSOA11CD <- domelec_LSOA_2010$`Lower Layer Super Output Area (LSOA) Code`
domelec_LSOA_2019$LSOA11CD <- domelec_LSOA_2019$`Lower Layer Super Output Area (LSOA) Code`
merged_de <- merge(domelec_LSOA_2010, domelec_LSOA_2019, by="Lower Layer Super Output Area (LSOA) Code", all=TRUE)
names(merged_de)## [1] "Lower Layer Super Output Area (LSOA) Code"
## [2] "Local Authority Name.x"
## [3] "Local Authority Code.x"
## [4] "Middle Layer Super Output Area (MSOA) Name.x"
## [5] "Middle Layer Super Output Area (MSOA) Code.x"
## [6] "Lower Layer Super Output Area (LSOA) Name.x"
## [7] "Total number of domestic electricity meters.x"
## [8] "Total domestic electricity consumption (kWh).x"
## [9] "Mean domestic electricity consumption \n(kWh per meter).x"
## [10] "Median domestic electricity consumption \n(kWh per meter).x"
## [11] "de2010"
## [12] "LSOA11CD.x"
## [13] "Local Authority Name.y"
## [14] "Local Authority Code.y"
## [15] "Middle Layer Super Output Area (MSOA) Name.y"
## [16] "Middle Layer Super Output Area (MSOA) Code.y"
## [17] "Lower Layer Super Output Area (LSOA) Name.y"
## [18] "Total number of domestic electricity meters.y"
## [19] "Total domestic electricity consumption (kWh).y"
## [20] "Mean domestic electricity consumption \n(kWh per meter).y"
## [21] "Median domestic electricity consumption \n(kWh per meter).y"
## [22] "de2019"
## [23] "LSOA11CD.y"merged_de$de_diff<-as.numeric(merged_de$de2019)-as.numeric(merged_de$de2010)
summary(merged_de)## Lower Layer Super Output Area (LSOA) Code Local Authority Name.x
## Length:41730 Length:41730
## Class :character Class :character
## Mode :character Mode :character
##
##
##
##
## Local Authority Code.x Middle Layer Super Output Area (MSOA) Name.x
## Length:41730 Length:41730
## Class :character Class :character
## Mode :character Mode :character
##
##
##
##
## Middle Layer Super Output Area (MSOA) Code.x
## Length:41730
## Class :character
## Mode :character
##
##
##
##
## Lower Layer Super Output Area (LSOA) Name.x
## Length:41730
## Class :character
## Mode :character
##
##
##
##
## Total number of domestic electricity meters.x
## Min. : 21
## 1st Qu.: 544
## Median : 652
## Mean : 652
## 3rd Qu.: 749
## Max. :48893
##
## Total domestic electricity consumption (kWh).x
## Min. : 103945
## 1st Qu.: 2168495
## Median : 2580372
## Mean : 2704421
## 3rd Qu.: 3101820
## Max. :191175966
##
## Mean domestic electricity consumption \n(kWh per meter).x
## Min. : 1462
## 1st Qu.: 3583
## Median : 3968
## Mean : 4169
## 3rd Qu.: 4497
## Max. :13360
##
## Median domestic electricity consumption \n(kWh per meter).x de2010
## Min. : 250 Min. : 1462
## 1st Qu.: 2988 1st Qu.: 3583
## Median : 3335 Median : 3968
## Mean : 3441 Mean : 4169
## 3rd Qu.: 3758 3rd Qu.: 4497
## Max. :10533 Max. :13360
##
## LSOA11CD.x Local Authority Name.y Local Authority Code.y
## Length:41730 Length:41730 Length:41730
## Class :character Class :character Class :character
## Mode :character Mode :character Mode :character
##
##
##
##
## Middle Layer Super Output Area (MSOA) Name.y
## Length:41730
## Class :character
## Mode :character
##
##
##
##
## Middle Layer Super Output Area (MSOA) Code.y
## Length:41730
## Class :character
## Mode :character
##
##
##
##
## Lower Layer Super Output Area (LSOA) Name.y
## Length:41730
## Class :character
## Mode :character
##
##
##
##
## Total number of domestic electricity meters.y
## Min. : 43.0
## 1st Qu.: 560.0
## Median : 670.0
## Mean : 688.1
## 3rd Qu.: 790.0
## Max. :47236.0
## NA's :3
## Total domestic electricity consumption (kWh).y
## Min. : 149948
## 1st Qu.: 1905454
## Median : 2300776
## Mean : 2462117
## 3rd Qu.: 2850550
## Max. :146809165
## NA's :3
## Mean domestic electricity consumption \n(kWh per meter).y
## Min. : 1268
## 1st Qu.: 3058
## Median : 3392
## Mean : 3586
## 3rd Qu.: 3848
## Max. :14094
## NA's :3
## Median domestic electricity consumption \n(kWh per meter).y de2019
## Min. : 806.3 Min. : 1268
## 1st Qu.:2530.4 1st Qu.: 3058
## Median :2814.3 Median : 3392
## Mean :2902.6 Mean : 3586
## 3rd Qu.:3151.8 3rd Qu.: 3848
## Max. :8731.5 Max. :14094
## NA's :3 NA's :3
## LSOA11CD.y de_diff
## Length:41730 Min. :-5873.3
## Class :character 1st Qu.: -711.7
## Mode :character Median : -553.2
## Mean : -582.8
## 3rd Qu.: -411.2
## Max. : 2456.0
## NA's :3# merge to sf geometry on LSOA11CD
merged_de$LSOA11CD <- merged_de$"Lower Layer Super Output Area (LSOA) Code"
lsoa_sf_de20102019<-merge(lsoa_sf_data, merged_de)
ggplot2::ggplot(lsoa_sf_de20102019) +
geom_sf(aes(fill=de_diff))+
scale_fill_continuous(name="Change in Mean consumpton (kWh per meter)",low="green",high="red") +
labs(caption="Solent(all LSOAs)")
lsoa_sf_de20102019$pc_diff <- 100*(lsoa_sf_de20102019$de_diff/lsoa_sf_de20102019$de2010)
t_diff <- dplyr::select(as.data.frame(lsoa_sf_de20102019),
LSOA11CD, `Lower Layer Super Output Area (LSOA) Name.y`,
de2010, de2019, de_diff, pc_diff)
head(arrange(t_diff, pc_diff))## LSOA11CD Lower Layer Super Output Area (LSOA) Name.y de2010 de2019
## 1 E01023199 Test Valley 013D 6265.640 3940.512
## 2 E01023201 Test Valley 003B 4715.905 2992.417
## 3 E01022546 Basingstoke and Deane 005D 5373.513 3416.407
## 4 E01017278 Southampton 031D 3772.492 2439.883
## 5 E01023224 Winchester 014A 6119.198 3960.261
## 6 E01022653 Eastleigh 010A 5346.714 3549.718
## de_diff pc_diff
## 1 -2325.128 -37.10919
## 2 -1723.488 -36.54628
## 3 -1957.106 -36.42134
## 4 -1332.610 -35.32438
## 5 -2158.938 -35.28138
## 6 -1796.996 -33.60935head(arrange(t_diff, -pc_diff))## LSOA11CD Lower Layer Super Output Area (LSOA) Name.y de2010 de2019
## 1 E01017032 Portsmouth 016B 3019.755 3296.955
## 2 E01032842 Basingstoke and Deane 011E 4437.491 4738.937
## 3 E01022738 Fareham 011D 2729.289 2891.538
## 4 E01022631 East Hampshire 010D 6112.007 6375.912
## 5 E01022489 Basingstoke and Deane 003B 7585.273 7845.624
## 6 E01022592 East Hampshire 011A 7172.338 7403.228
## de_diff pc_diff
## 1 277.2005 9.179569
## 2 301.4457 6.793156
## 3 162.2488 5.944728
## 4 263.9044 4.317803
## 5 260.3506 3.432317
## 6 230.8898 3.219171ggplot2::ggplot(t_diff, aes(x = pc_diff)) +
geom_histogram()## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
####Map with Overlay
library(leaflet)
st_coord_sys <- st_crs(lsoa_sf_de20102019)
st_coord_sys$epsg## [1] 27700#Transforming the coord system
if(st_coord_sys$epsg !=4326) {
map_df_trans <- st_transform(lsoa_sf_de20102019, "+proj=longlat +datum=WGS84")
}qpal <- colorNumeric("Reds", map_df_trans$de_diff, n=9)
leaflet(map_df_trans) %>%
addTiles() %>% # Add default OpenStreetMap map tiles
addPolygons(color = ~qpal(de_diff),
fillOpacity = 0.6, weight = 1.5, popup = ~(LSOA11CD ),
highlight = highlightOptions(
weight = 5,
color = "#666",
fillOpacity = 0.7,
bringToFront = TRUE))#####Cities in more detail
#Cities such as Southampton disappear due to their density, we will now map Southampton alone to see this area in more detail
mapData <- dplyr::filter(lsoa_sf_de20102019, LAD11NM == "Southampton")
# plotting the electricity consumption of Southampton in more detail
ggplot2::ggplot(mapData) +
geom_sf(aes(fill= de_diff)) +
scale_fill_continuous(name = "Change in Mean consumpton (kWh per meter)", low = "green", high = "red") +
labs(caption = "Southampton")
#BROKEN#Cities such as Portsmouth disappear due to their density, we will now map Portsmouth alone to see this area in more detail
mapData <- dplyr::filter(lsoa_sf_de20102019, LAD11NM == "Portsmouth")
# plotting the electricity consumption of Portsmouth in more detail
ggplot2::ggplot(mapData) +
geom_sf(aes(fill = de_diff)) +
scale_fill_continuous(name = "Change in Mean comsumpton (kWh per meter)", low = "green", high = "red") +
labs(caption = "Portsmouth")
#Cities such as Winchester disappear due to their density, we will now map Winchester alone to see this area in more detail
mapData <- dplyr::filter(lsoa_sf_de20102019, LAD11NM == "Winchester")
# plotting the electricity consumption of Winchester in more detail
ggplot2::ggplot(mapData) +
geom_sf(aes(fill = de_diff)) +
scale_fill_continuous(name = "Change in Mean comsumpton (kWh per meter)", low = "green", high = "red") +
labs(caption = "Winchester")
1.1.2 Mapping Gas
####2019
#Electricty consumption data LSOA pre downloaded
inFile <-paste0(dataFolder, "energy/gas/LSOA Gas csv/LSOA_GAS_2019.csv")
lsoa_gasData <- readr::read_csv(inFile)##
## -- Column specification --------------------------------------------------------
## cols(
## `Local Authority Name` = col_character(),
## `Local Authority Code` = col_character(),
## `MSOA Name` = col_character(),
## `Middle Layer Super Output Area (MSOA) Code` = col_character(),
## `LSOA Name` = col_character(),
## `Lower Layer Super Output Area (LSOA) Code` = col_character(),
## `Number of consuming meters` = col_double(),
## `Consumption (kWh)` = col_double(),
## `Mean consumption (kWh per meter)` = col_double(),
## `Median consumption (kWh per meter)` = col_double(),
## `Number of non-consuming meters` = col_character()
## )lsoa_gasData$LSOA11CD <- lsoa_gasData$`Lower Layer Super Output Area (LSOA) Code`
head(lsoa_gasData)## # A tibble: 6 x 12
## `Local Authority~ `Local Authority~ `MSOA Name` `Middle Layer Sup~ `LSOA Name`
## <chr> <chr> <chr> <chr> <chr>
## 1 Hartlepool E06000001 Hartlepool~ E02002483 Hartlepool~
## 2 Hartlepool E06000001 Hartlepool~ E02002483 Hartlepool~
## 3 Hartlepool E06000001 Hartlepool~ E02002483 Hartlepool~
## 4 Hartlepool E06000001 Hartlepool~ E02002483 Hartlepool~
## 5 Hartlepool E06000001 Hartlepool~ E02002483 Hartlepool~
## 6 Hartlepool E06000001 Hartlepool~ E02002483 Hartlepool~
## # ... with 7 more variables: Lower Layer Super Output Area (LSOA) Code <chr>,
## # Number of consuming meters <dbl>, Consumption (kWh) <dbl>,
## # Mean consumption (kWh per meter) <dbl>,
## # Median consumption (kWh per meter) <dbl>,
## # Number of non-consuming meters <chr>, LSOA11CD <chr>lsoa_merged_sf_gas <- merge(lsoa_sf_data, lsoa_gasData) #merging the boundaries and energy data
ggplot2::ggplot(lsoa_merged_sf_gas) +
geom_sf(aes(fill = `Mean consumption (kWh per meter)`)) +
scale_fill_continuous(name = "Gas: Mean kWh per meter", low = "green", high = "red") +
labs(caption = "Solent (all LSOAs)")
#Mapping itAha - this one shows where there is no gas :-)
###Map with Overlay
library(leaflet)
st_coord_sys <- st_crs(lsoa_merged_sf_gas)
st_coord_sys$epsg ## [1] 27700#Transforming the coord system
if(st_coord_sys$epsg !=4326){
map_df_trans <- st_transform(lsoa_merged_sf_gas, "+proj=longlat +datum=WGS84")
}qpal <- colorNumeric("Reds", map_df_trans$`Mean consumption (kWh per meter)`, n=9)
leaflet(map_df_trans) %>%
addTiles() %>%
addPolygons(color= ~qpal(`Mean consumption (kWh per meter)`),
fillOpacity = 0.6, weight = 1.5, popup = ~(LSOA11CD),
highlight=highlightOptions(
weight=5,
color="#666",
fillOpacity = 0.7,
bringToFront = TRUE))#####Cities in More detail
#Cities such as Southampton disappear due to their density, we will now map Southampton alone to see this area in more detail
mapData <- dplyr::filter(lsoa_merged_sf_gas, LAD11NM == "Southampton")
# plotting the gas consumption of Southampton in more detail
ggplot2::ggplot(mapData) +
geom_sf(aes(fill = `Mean consumption (kWh per meter)`)) +
scale_fill_continuous(name = "Gas: Mean kWh per meter", low = "green", high = "red") +
labs(caption = "Southampton")
#Cities such as Portsmouth disappear due to their density, we will now map Portsmouth alone to see this area in more detail
mapData <- dplyr::filter(lsoa_merged_sf_gas, LAD11NM == "Portsmouth")
# plotting the gas consumption of Portsmouth in more detail
ggplot2::ggplot(mapData) +
geom_sf(aes(fill = `Mean consumption (kWh per meter)`)) +
scale_fill_continuous(name = "Gas: Mean kWh per meter", low = "green", high = "red") +
labs(caption = "Portsmouth")
#Cities such as Winchester disappear due to their density, we will now map Winchester alone to see this area in more detail
mapData <- dplyr::filter(lsoa_merged_sf_gas, LAD11NM == "Winchester")
# plotting the gas consumption of Winchester in more detail
ggplot2::ggplot(mapData) +
geom_sf(aes(fill = `Mean consumption (kWh per meter)`)) +
scale_fill_continuous(name = "Gas: Mean kWh per meter", low = "green", high = "red") +
labs(caption = "Winchester")
####Difference between 2010 and 2019 dom gas
#Green indicates a decrease in gas consumption from 2010 to 2019
#Red indicates an increase in gas consumption from 2010 to 2019
dgas_LSOA_2010 <- readr::read_csv(paste0(dataFolder, "energy/gas/LSOA Gas csv/LSOA_GAS_2010.csv"))##
## -- Column specification --------------------------------------------------------
## cols(
## `Local Authority Name` = col_character(),
## `Local Authority Code` = col_character(),
## `MSOA Name` = col_character(),
## `Middle Layer Super Output Area (MSOA) Code` = col_character(),
## `LSOA Name` = col_character(),
## `Lower Layer Super Output Area (LSOA) Code` = col_character(),
## `Number of meters` = col_double(),
## `Consumption (kWh)` = col_double(),
## `Mean consumption (kWh per meter)` = col_double(),
## `Median consumption (kWh per meter)` = col_double()
## )dgas_LSOA_2019 <- readr::read_csv(paste0(dataFolder, "energy/gas/LSOA Gas csv/LSOA_GAS_2019.csv"))##
## -- Column specification --------------------------------------------------------
## cols(
## `Local Authority Name` = col_character(),
## `Local Authority Code` = col_character(),
## `MSOA Name` = col_character(),
## `Middle Layer Super Output Area (MSOA) Code` = col_character(),
## `LSOA Name` = col_character(),
## `Lower Layer Super Output Area (LSOA) Code` = col_character(),
## `Number of consuming meters` = col_double(),
## `Consumption (kWh)` = col_double(),
## `Mean consumption (kWh per meter)` = col_double(),
## `Median consumption (kWh per meter)` = col_double(),
## `Number of non-consuming meters` = col_character()
## )dgas_LSOA_2010$dgas2010 <- dgas_LSOA_2010$'Mean consumption (kWh per meter)'
dgas_LSOA_2019$dgas2019 <- dgas_LSOA_2019$'Mean consumption (kWh per meter)'
merged_dgas <- merge(dgas_LSOA_2010, dgas_LSOA_2019, by = "Lower Layer Super Output Area (LSOA) Code", all = TRUE)
names(merged_dgas)## [1] "Lower Layer Super Output Area (LSOA) Code"
## [2] "Local Authority Name.x"
## [3] "Local Authority Code.x"
## [4] "MSOA Name.x"
## [5] "Middle Layer Super Output Area (MSOA) Code.x"
## [6] "LSOA Name.x"
## [7] "Number of meters"
## [8] "Consumption (kWh).x"
## [9] "Mean consumption (kWh per meter).x"
## [10] "Median consumption (kWh per meter).x"
## [11] "dgas2010"
## [12] "Local Authority Name.y"
## [13] "Local Authority Code.y"
## [14] "MSOA Name.y"
## [15] "Middle Layer Super Output Area (MSOA) Code.y"
## [16] "LSOA Name.y"
## [17] "Number of consuming meters"
## [18] "Consumption (kWh).y"
## [19] "Mean consumption (kWh per meter).y"
## [20] "Median consumption (kWh per meter).y"
## [21] "Number of non-consuming meters"
## [22] "dgas2019"merged_dgas$dg_diff<-as.numeric(merged_dgas$dgas2019) - merged_dgas$dgas2010
summary(merged_dgas)## Lower Layer Super Output Area (LSOA) Code Local Authority Name.x
## Length:40226 Length:40226
## Class :character Class :character
## Mode :character Mode :character
##
##
##
##
## Local Authority Code.x MSOA Name.x
## Length:40226 Length:40226
## Class :character Class :character
## Mode :character Mode :character
##
##
##
##
## Middle Layer Super Output Area (MSOA) Code.x LSOA Name.x
## Length:40226 Length:40226
## Class :character Class :character
## Mode :character Mode :character
##
##
##
##
## Number of meters Consumption (kWh).x Mean consumption (kWh per meter).x
## Min. : 6.0 Min. : 56289 Min. : 553.6
## 1st Qu.: 475.0 1st Qu.: 6614181 1st Qu.:12997.8
## Median : 596.0 Median : 8662085 Median :14915.0
## Mean : 566.2 Mean : 8581876 Mean :15444.4
## 3rd Qu.: 673.0 3rd Qu.: 10475081 3rd Qu.:17241.8
## Max. :67568.0 Max. :931766817 Max. :52729.2
## NA's :104 NA's :104 NA's :104
## Median consumption (kWh per meter).x dgas2010 Local Authority Name.y
## Min. : 1 Min. : 553.6 Length:40226
## 1st Qu.:11957 1st Qu.:12997.8 Class :character
## Median :13870 Median :14915.0 Mode :character
## Mean :14290 Mean :15444.4
## 3rd Qu.:16058 3rd Qu.:17241.8
## Max. :56900 Max. :52729.2
## NA's :104 NA's :104
## Local Authority Code.y MSOA Name.y
## Length:40226 Length:40226
## Class :character Class :character
## Mode :character Mode :character
##
##
##
##
## Middle Layer Super Output Area (MSOA) Code.y LSOA Name.y
## Length:40226 Length:40226
## Class :character Class :character
## Mode :character Mode :character
##
##
##
##
## Number of consuming meters Consumption (kWh).y
## Min. : 5.0 Min. :1.860e+04
## 1st Qu.: 492.0 1st Qu.:6.166e+06
## Median : 615.0 Median :8.062e+06
## Mean : 599.2 Mean :8.087e+06
## 3rd Qu.: 705.0 3rd Qu.:9.839e+06
## Max. :87991.0 Max. :1.018e+09
## NA's :13 NA's :13
## Mean consumption (kWh per meter).y Median consumption (kWh per meter).y
## Min. : 1193 Min. : 537.1
## 1st Qu.:11476 1st Qu.:10361.9
## Median :13197 Median :12045.5
## Mean :13768 Mean :12531.2
## 3rd Qu.:15384 3rd Qu.:14090.4
## Max. :47003 Max. :49456.0
## NA's :13 NA's :13
## Number of non-consuming meters dgas2019 dg_diff
## Length:40226 Min. : 1193 Min. :-22666
## Class :character 1st Qu.:11476 1st Qu.: -2090
## Mode :character Median :13197 Median : -1601
## Mean :13768 Mean : -1676
## 3rd Qu.:15384 3rd Qu.: -1143
## Max. :47003 Max. : 23242
## NA's :13 NA's :117merged_dgas$LSOA11CD <- merged_dgas$"Lower Layer Super Output Area (LSOA) Code" #creating a variable with the LSOA code in the same name as in sf_data
lsoa_dgas20102019 <- merge(lsoa_sf_data, merged_dgas) #merging these
ggplot2::ggplot(lsoa_dgas20102019) + geom_sf(aes(fill=dg_diff))+
scale_fill_continuous(name="Change in Gas: Mean kWh per meter", low="green",high="red")+
labs(caption ="Solent (all MSOAs)") ##BROKEN
##Need to change the varibale names to reflect what is actually there####Map with overlay
library(leaflet)
st_coord_sys <- st_crs(lsoa_dgas20102019)
st_coord_sys$epsg## [1] 27700#Transforming the coor system
if(st_coord_sys$epsg !=4326){
map_df_trans <- st_transform(lsoa_dgas20102019, "+proj=longlat +datum=WGS84")
}qpal <- colorNumeric("Reds", map_df_trans$dg_diff, n=9)
leaflet(map_df_trans) %>%
addTiles() %>%
addPolygons(color = ~qpal(dg_diff),
fillOpacity = 0.6, weight = 1.5, popup = ~(LSOA11CD),
highlight = highlightOptions(
weight = 5,
color = "#666",
fillOpacity = 0.7,
bringToFront = TRUE))#####Cities in more detail
#Cities such as Southampton disappear due to their density, we will now map Southampton alone to see this area in more detail
mapData <- dplyr::filter(lsoa_dgas20102019, LAD11NM == "Southampton")
# plotting the gas consumption of Southampton in more detail
ggplot2::ggplot(mapData) +
geom_sf(aes(fill = dg_diff)) +
scale_fill_continuous(name = "Gas: Mean kWh per meter", low = "green", high = "red") +
labs(caption = "Southampton")
#Cities such as Portsmouth disappear due to their density, we will now map Portsmouth alone to see this area in more detail
mapData <- dplyr::filter(lsoa_dgas20102019, LAD11NM == "Portsmouth")
# plotting the gas consumption of Portsmouth in more detail
ggplot2::ggplot(mapData) +
geom_sf(aes(fill = dg_diff)) +
scale_fill_continuous(name = "Gas: Mean kWh per meter", low = "green", high = "red") +
labs(caption = "Portsmouth")
#Cities such as Winchester disappear due to their density, we will now map Winchester alone to see this area in more detail
mapData <- dplyr::filter(lsoa_dgas20102019, LAD11NM == "Winchester")
# plotting the gas consumption of Winchester in more detail
ggplot2::ggplot(mapData) +
geom_sf(aes(fill = dg_diff)) +
scale_fill_continuous(name = "Gas: Mean kWh per meter", low = "green", high = "red") +
labs(caption = "Winchester")